Comparison between the ages for the genders (after controlling for type)¶

Questions

What are the differences between the ages for the different genders?
Do we observe the same changes as globally?

Age effect - General Questions

What are the differences between the ages?
Which genes and pathways are differentially expressed between 8w and 52w, between 52w and 104w, between 8w and 104w? Are they the same? Is there a gradient?
Are they different for the two genders?
Are they different for the two types?

Loads¶

Libraries and functions¶

Warning message in is.na(x[[i]]):
“is.na() applied to non-(list or vector) of type 'environment'”Warning message in rsqlite_fetch(res@ptr, n = n):
“Don't need to call dbFetch() for statements, only for queries”

==========================================================================
*
*  Package WGCNA 1.63 loaded.
*
*    Important note: It appears that your system supports multi-threading,
*    but it is not enabled within WGCNA in R. 
*    To allow multi-threading within WGCNA with all available cores, use 
*
*          allowWGCNAThreads()
*
*    within R. Use disableWGCNAThreads() to disable threading if necessary.
*    Alternatively, set the following environment variable on your system:
*
*          ALLOW_WGCNA_THREADS=<number_of_processors>
*
*    for example 
*
*          ALLOW_WGCNA_THREADS=4
*
*    To set the environment variable in linux bash shell, type 
*
*           export ALLOW_WGCNA_THREADS=4
*
*     before running R. Other operating systems or shells will
*     have a similar command to achieve the same aim.
*
==========================================================================


Allowing multi-threading with up to 4 threads.
[1] "preparing gene to GO mapping data..."
[1] "preparing IC data..."
[1] "preparing gene to GO mapping data..."
[1] "preparing IC data..."
[1] "preparing gene to GO mapping data..."
[1] "preparing IC data..."

Data¶

Stats¶

Differentially expressed genes¶

  52w VS 8w (F)   52w VS 8w (M) 104w VS 52w (F) 104w VS 52w (M)  104w VS 8w (F) 
      0.4021071       0.6557377       0.6620690       0.4093492       0.5192830 
 104w VS 8w (M) 
      0.4932542

Warning message in pcls(G):
“initial point very close to some inequality constraints”Warning message in pcls(G):
“initial point very close to some inequality constraints”Warning message in pcls(G):
“initial point very close to some inequality constraints”Warning message in pcls(G):
“initial point very close to some inequality constraints”Warning message in pcls(G):
“initial point very close to some inequality constraints”Warning message in pcls(G):
“initial point very close to some inequality constraints”Warning message in stack.default(getgo(rownames(l$deg), "mm10", "geneSymbol")):
“non-vector elements will be ignored”Warning message in stack.default(getgo(rownames(as.data.frame(l$deg)), "mm10", "geneSymbol", :
“non-vector elements will be ignored”

Counts¶

52w != 8w or 104w != 8w or 104w != 52w for F with abs(FC) > 2

52w != 8w or 104w != 8w or 104w != 52w for M with abs(FC) > 2

Genes with 52w != 8w or 104w != 8w or 104w != 52w for M or F and at least one FC > 2

Comparison of the numbers per types¶

Differentially expressed genes

Some explanation (specially for the gender difference in microglia aging)

Differentially more expressed genes

Differentially less expressed genes

DEG into gene co-expression network¶

White: up-regulated
Black: down-regulated

Comp	Male	Female
52w vs 8w
104w vs 52w
104w vs 8w

GO analysis¶

Biological process¶

Dot-plot with the most over-represented BP GO (20 most significant p-values for the different comparison)

Using term, id as id variables
Using term, id as id variables

Network based on description similarity

Comp	Male	Female
52w vs 8w
104w vs 52w
104w vs 8w

52w VS 8w (F)¶

GO Tree at "../results/dge/age-effect/age_gender/go/52w_VS_8w_F.png"

52w VS 8w (M)¶

GO Tree at "../results/dge/age-effect/age_gender/go/52w_VS_8w_M.png"

104w VS 52w (F)¶

GO Tree at "../results/dge/age-effect/age_gender/go/104w_VS_52w_F.png"

104w VS 52w (M)¶

GO Tree at "../results/dge/age-effect/age_gender/go/104w_VS_52w_M.png"

Cellular components¶

Dot-plot with the most over-represented CC GO (20 most significant p-values for the different comparison)

Using term, id as id variables
Using term, id as id variables

Comp	Female	Male
52w vs 8w
104w vs 52w
104w vs 8w

Molecular functions¶

Dot-plot with the most over-represented MF GO (20 most significant p-values for the different comparison)

Using term, id as id variables
Using term, id as id variables

Comp	Female	Male
52w vs 8w
104w vs 52w
104w vs 8w

KEGG pathways¶

Error in `$<-.data.frame`(`*tmp*`, labels, value = c("", "", "", "", "", : replacement has 12 rows, data has 28
Traceback:

1. plot_kegg_pathways(age_gender_deg$over_represented_KEGG[, "category"], 
 .     age_gender_deg$fc_deg, "../results/dge/age-effect/age_gender/kegg/over_repr_kegg/")
2. suppressMessages(pathview(gene.data = fc_deg, pathway.id = cat, 
 .     species = "Mus musculus", gene.idtype = "Symbol"))
3. withCallingHandlers(expr, message = function(c) invokeRestart("muffleMessage"))
4. pathview(gene.data = fc_deg, pathway.id = cat, species = "Mus musculus", 
 .     gene.idtype = "Symbol")
5. `$<-`(`*tmp*`, labels, value = c("", "", "", "", "", "", "", 
 . "", "", "", "", ""))
6. `$<-.data.frame`(`*tmp*`, labels, value = c("", "", "", "", "", 
 . "", "", "", "", "", "", ""))
7. stop(sprintf(ngettext(N, "replacement has %d row, data has %d", 
 .     "replacement has %d rows, data has %d"), N, nrows), domain = NA)

Gender differences in aging¶

Question: Is there differences in aging between gender? Is there really a delay for some genes in male?

	52w vs 8w for F	52w vs 8w for M	104w vs 52w for F	104w vs 52w for M	Gene number
Set 1	!=	==	==	!=	729
Set 2	==	!=	!=	==	9

Genes with differential expression delayed in male¶

Genes (set 1):

Differentially expressed between 52w and 8w in F
Not differentially expressed between 104w and 52w in F
Not differentially expressed between 52w and 8w in M
Differentially expressed between 104w and 52w in M
No criteria for 104w vs 8w in both F and M

52w VS 8w (F) > 0 and 104w VS 52w (M) > 0¶

[1] 186

	Pearson's product-moment correlation

data:  mat[, 1] and mat[, 2]
t = 34.483, df = 184, p-value < 2.2e-16
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 0.9083259 0.9475915
sample estimates:
      cor 
0.9305874

52w VS 8w (F) > 0 and 104w VS 52w (M) < 0¶

[1] 136

	Pearson's product-moment correlation

data:  mat[, 1] and mat[, 2]
t = -26.39, df = 134, p-value < 2.2e-16
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 -0.9393075 -0.8836611
sample estimates:
       cor 
-0.9157734

52w VS 8w (F) < 0 and 104w VS 52w (M) > 0¶

[1] 267

	Pearson's product-moment correlation

data:  mat[, 1] and mat[, 2]
t = -8.3658, df = 265, p-value = 3.45e-15
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 -0.5471083 -0.3566042
sample estimates:
       cor 
-0.4570828

52w VS 8w (F) < 0 and 104w VS 52w (M) < 0¶

[1] 140

	Pearson's product-moment correlation

data:  mat[, 1] and mat[, 2]
t = 16.17, df = 138, p-value < 2.2e-16
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 0.7428457 0.8595714
sample estimates:
      cor 
0.8090422

Summary¶

Comp	52w VS 8w (F) < 0	52w VS 8w (F) > 0
104w VS 52w (M) > 0
104w VS 52w (M) < 0

Genes with differential expression delayed in female¶

Genes (set 2):

Not differentially expressed between 52w and 8w in F
Differentially expressed between 104w and 52w in F
Differentially expressed between 52w and 8w in M
Not differentially expressed between 104w and 52w in M
No criteria for 104w vs 8w in both F and M

	Wald padj < 0.05	LFC > 0 (Wald padj < 0.05)	LFC < 0 (Wald padj < 0.05)
52w VS 8w (F)	2278	1265	1013
52w VS 8w (M)	427	261	166
104w VS 52w (F)	290	169	121
104w VS 52w (M)	3872	1974	1898
104w VS 8w (F)	1841	1074	767
104w VS 8w (M)	3706	1925	1781